Over the past decades, the successive Coupled Model Intercomparison Projects
(CMIPs) have produced a huge amount of global climate model simulations.
Along these years, the climate models have advanced and can thus provide
credible evolution of climate at least at continental or global scales since
they are better representing physical processes and feedbacks in the climate
system. Nevertheless, due to the coarse horizontal resolution of global
climate models, it is necessary to downscale these results for their use to
assess possible future impacts of climate change in climate sensitive
ecosystems and sectors and to adopt adaptation strategies at local and
national level. In this vein, the Spanish State Meteorological Agency (AEMET)
has been producing since 2006 a set of reference downscaled climate change
projections over Spain either applying statistical downscaling techniques to
the outputs of the Global Climate Models (GCMs) or making use of the
information generated by dynamical downscaling techniques through European
projects or international initiatives such as PRUDENCE, ENSEMBLES and
EURO-CORDEX. The AEMET strategy aims at exploiting all the available sources
of information on climate change projections. The generalized use of
statistical and dynamical downscaling approaches allow us to encompass a
great number of global models and therefore to provide a better estimation of
uncertainty. Most impact climate change studies over Spain make use of this
reference downscaled projections emphasizing the estimation of uncertainties.
Additionally to the rationale and history behind the AEMET generation of
climate change scenarios, we focus on some preliminary analysis of the
dependency of estimated uncertainties on the different sources of data.

Climate change constitutes one of the main global threats we must face in
the present century. Even when considering the most optimistic outlooks
regarding future greenhouse gas (GHG) emissions, scientific studies reveal
that some climate change is inevitable (IPCC, 2013). The increased
concentration of GHGs in the atmosphere causes modification of several
climate parameters, which in turn are responsible for environmental changes
that might result in shifts in the ecosystems and the social and economic
systems and sectors. The direction, amount and intensity of climate
alterations will, in the end, determine the definitive trends and magnitudes
of the impacts at the local, regional and planetary levels.

In this context, society demands more and more climate information at local
levels but, at the same time, we are aware that though climate models have
experienced great changes, there are still biases with highest impact at
small scales (Bruyère et al., 2014). Besides, mainland Spain and Balearic
Islands are located in the western part of the Mediterranean Basin,
recognized as a “climate change hot spot” that could be strongly affected
in the future by significant warming and drying (Giorgi, 2006).

Therefore, to complement the efforts in the reduction of GHG emissions, it is
necessary to adopt and implement whatever adaptation measures aimed at
reducing the vulnerability of our ecosystems and sectors at the relevant
scales and decision levels, in order to minimize its negative impacts. So,
studies and measures in climate change adaptation are boosting activity in
regionalized climate change projections. Many ad hoc institutes have
been created to meet this demand and/or National Meteorological and
Hydrological Services (NMHSs) are additionally tasked with this new area of
activity.

Furthermore, the interest in estimating the potential socioeconomic costs of
climate change has led to the increasing use of either dynamical or
statistical downscaling methods to produce finer spatial and time scale
climate projections for the impact community. In this frame, AEMET, in the
division of climate services, has produced a new collection of regionalized
climate change projections. In the corresponding web portal users can get an
idea of various aspects of climate change from a suite of maps, diagrams,
explanatory texts and users' guides. The manuscript is structured as follows.
Section 2 gives a comprehensive description of preliminaries and time
milestones while the strategy is presented in Sect. 3, focusing on the main
contents of the webpage together with the climate projections from CMIP3 and
CMIP5 (the third and fifth phases of CMIP) respectively) climate models.
Finally, some concluding remarks are summarized in Sect. 4.

In 2006, routine production of regionalized/downscaled climate change
projections at century-scale for Spain was launched as a consequence of the
international climate change negotiations in the frame of United Nations
Framework Convention on Climate Change (UNFCCC) and the commitments there
assumed by the Parties. Under the umbrella of the Spanish National
Adaptation Plan to Climate Change (PNACC), AEMET was mandated to develop the
production and update of such projections in coordination with the academic
and research community. PNACC is the general reference frame tool for the
coordination of Public Administrations' efforts dealing with
the assessment of impacts, vulnerability, and adaptation to climate change
in the Spanish sectors acknowledged as potentially affected (water
management, agriculture, forests, biodiversity, coasts, health, tourism,
etc.).

PNACC provides tools for the elaboration of diagnoses and the development of
more efficient ways for adaptation and it is actually a process to guide the
activities of Public Administrations, enterprises and stakeholders towards a
common objective, committing themselves to the fight against climate change.
Figure 1 depicts the structure of the PNACC. We must be aware that the PNACC
will only be effective if its existence, progress and results are
disseminated and communicated in an effective way to all the relevant
stakeholders. Its main original objectives include: (a) to develop the future
regional climate scenarios for the Spanish geography, (b) to develop and
apply methods and tools to evaluate impacts, vulnerability and the
adaptation to climate change for all the relevant socioeconomic sectors and
ecological systems, (c) to incorporate into the Spanish Research &
Development & innovation (R&D&i) system the most relevant needs for
climate change impact assessment, (d) to carry out continuous information and
communication activities about the projects, (e) to promote the participation
of all stakeholders involved in the different sectors and systems, for
purposes of mainstreaming adaptation to climate change to sector policies,
and (f) to prepare specific reports on the results of the evaluations and
projects, and periodical follow-up reports about the projects and the
National Adaptation Plan as a whole (see for more details,
https://www.mapama.gob.es/es/cambio-climatico/temas/impactos-vulnerabilidad-y-adaptacion/folleto pnacc_tcm30-70394.pdf,
last access: 3 August 2018).

2.1 Milestones

The timelines showing the major milestones are summarized in Table 1.
Briefly, in 2006, AEMET was mandated in the frame of PNACC to develop the
routine production and update of downscaled climate change projections for
Spain at century-scale, in coordination with the Spanish academic and
research community.

Our potential users were mainly channelled through the Spanish Climate Change
Office (OECC), the Hydrographic Studies Center (CEDEX)
(http://www.cedex.es/CEDEX/lang_castellano/, last access: 29 May 2018),
the Biodiversity Foundation, and other generic users. In 2008, took place the
first delivery (Brunet et al., 2008) of the periodical revision of regional
projections with the best data available from climate models contributing to
the Third Assessment Report (TAR), observations and regionalization
techniques from two international European projects, PRUDENCE
(http://prudence.dmi.dk, last access: 29 May 2018), and ENSEMBLES
(https://www.ecmwf.int/en/research/projects/ensembles, last access:
30 May 2018). These latter projects aimed at creating state-of-the-art
simulations performed with several Regional Climate Models (RCMs) driven by
several GCMs that would enable evaluation of uncertainties in RCM outputs and
provide data for climate change studies over Europe. The PRUDENCE project was
accomplished in 2005 (Christensen and Christensen, 2007; Jacob et al., 2007;
Déqué et al., 2007). ENSEMBLES was an EU-funded Integrated Project to
develop an ensemble prediction system for climate change based on the
principal state-of-the-art, high resolution, global and regional Earth System
models developed in Europe, being completed in December 2009. Detailed
information about the project and its results, including the RCM simulations,
can be found in van der Linden and Mitchell (2009). Apart from providing
standardised experiments for model intercomparisons, PRUDENCE and ENSEMBLES
were designed to create multimodel ensembles for sampling model uncertainties
(https://www.ecmwf.int/en/research/projects/ensembles, last access:
30 May 2018). The second delivery of regional projections took place in 2014
(Morata-Gasca, 2014), when the AEMET webpage published graphical results and
daily and monthly aggregated data; from ENSEMBLES project and two Spanish
national projects: ESCENA (Jiménez-Guerrero et al., 2013; Dominguez et
al., 2013), and ESTCENA (Gutiérrez et al., 2013), funded by the Spanish
Ministry of Environment devoted to dynamical and statistical downscaling,
respectively as well as downscaled data from CMIP3 climate models feeding the
Fourth Assessment Report (AR4). These two strategic actions of Plan Nacional
de R&D&i 2008–2011, contributed to the new version of the regional
climate change scenarios program PNACC-2012 within PNACC. In 2017, took place
the third delivery of regional projections by AEMET (Amblar-Francés et
al., 2017), focused mainly on statistical methods of regionalization applied
to CMIP5 climate models feeding the Fifth Assessment Report (AR5) and
dynamical regionalized projections from the EURO-CORDEX project. In this
delivery information aggregated by autonomous communities, provinces and
hydrographic basins was introduced.

Table 2Regionalized projections of temperature (Tmax & Tmin)
and precipitation obtained by statistical methods (analog and regression)
and dynamical regionalisation (EURO-CORDEX) based on AR5-IPCC results. For
analog and regression methods, numbers indicate the number of driving GCMs
in each case. For CORDEX, number refers to the number of different GCM/RCM
combinations.

Figure 3Temporal evolution of the annual change of warm nights for mainland
Spain (RCP4.5, RCP6.0, RCP8.5) for: (a) Analog, (b)
Regression and (b) EURO-CORDEX methods (a, b, c). In
parentheses, numbers of models considered in each RCP. In (d, e),
mean variation of this index for Autonomous Communities (AC) and Hydrographic
Basins (HB). Central line refers to the median of projections while bar is
the range between the 17th and the 83th percentiles.

In a nutshell, the strategy adopted by AEMET in the generation of Spanish
climate change projections
(http://www.aemet.es/es/serviciosclimaticos/cambio_climat, last access:
1 August 2018) is based on the exploitation of all relevant information based
on either statistical or dynamical downscaling techniques, generated either
by AEMET or other projects and international or national initiatives; with
strong emphasis on improving visualization together with easy way to access
information at appropriate scales (hydrographic basins (HB), provinces,
autonomous communities (AC), Spanish Iberia and Balearic and Canary Islands)
– see Figs. 2, 3. Table 2 shows the number of projections used in the 3rd
delivery obtained by two statistical methods (analog and regression), the
EURO-CORDEX dynamical regionalisation based on AR5-IPCC results, and three
Representative Concentration Pathways (RCPs): RCP4.5, RCP6.0 and RCP 8.5 for
statistical regionalization with two, RCP4.5 and RCP8.5, for dynamical
regionalization. RCP4.5 and RCP6.0 represent two intermediate stabilization
pathways in which radiative forcing is stabilized at approximately 4.5 and
6.0 W m−2 after 2100. As regards RCP8.5, radiative forcing reaches
>8.5 W m−2 by 2100 and continues to rise for some amount
of time.

Table 3 depicts the list of global models considered from CMIP5, while the
subset of models used from the EURO-CORDEX project is listed in Table 4. In
the AEMET webpage numerical information (daily and monthly scales) is
available from AR5 and AR4 and from relevant projects (EURO-CORDEX,
ENSEMBLES, ESCENA, ESTCENA etc.), different variables and extreme indicators
as well as graphic information. Table 5 shows the list of variables and
indicators that have been considered taking into account a variety of
sources (i.e., suggestions from specialized users, IPCC literature,
international projects, etc.).

Bridging the gap between the resolution of climate models and regional and
local scale processes represents a considerable challenge. Basically any
data can be refined by downscaling techniques (Rummukainen, 2010), this is a
crucial step for providing actionable information at the regional and local
scales required in impact and adaptation studies (Gutiérrez et al.,
2018). The main downscaling approaches are: dynamical downscaling (based on
regional climate models (RCM)) and empirical/statistical downscaling (ESD),
based on statistical models). As mentioned in Gutiérrez et al. (2018),
the relative merits and limitations of both dynamical and statistical
downscaling approaches have been widely discussed in the literature and it
is nowadays recognized that they are complementary in many practical
applications.

Dynamical downscaling represents a group of methods originally used in
numerical weather forecasting (Rummukainen, 2010; Maraun et al., 2010). Two
major streams are recognizable in dynamical downscaling: in the first, the
resolution is increased over the entire domain of the atmospheric global
model (e.g. Christensen and Christensen, 2007). The second strategy is based
on the utilization of a global model with variable grid size (Déqué
et al., 2012). Increasing resolution also entails increasing computational
cost and data volume. The more recent development proved that RCMs are
capable of delivering high resolution results (20 km or less), this adds
value in regions with variable orography, land-sea and other contrasts, as
well as in capturing sharp, short-duration and extreme events, understanding
that added value is usually defined as the ability of RCM simulations to
improve some specific aspects of the GCM simulations through the presence of
small scale features (Rummukainen, 2010). RCM also require a large expertise
in handle. For instance, before using RCM to examine the projected changes,
it is useful to evaluate to which extent the RCM outperforms its driving GCM
in faithfully simulating climatic features at regional scales. Due to these
practical limitations, the regional dynamical downscaling models remain out
of reach for a vast majority of impact researchers, involved in geographical,
biological, geological fields. Anyway, this is not the case for most climate
researchers that have the capacity to handle NetCDF files and daily data.

In the third delivery of climate regional projections by AEMET, dynamical
downscaling comes from EURO-CORDEX project (Jacob et al., 2014), since it
represents a fine scale set of climate simulations and it is openly
available; with multiple variables such as precipitation, maximum and
minimum temperatures, relative humidity and wind speed which are of interest
for impact studies.

Statistical downscaling is based on the perspective that regional climate is
mainly conditioned by two factors: the large-scale climate and the
local/regional features such as topography, land-sea distribution or
land-use (Fowler et al., 2007; Wilby et al., 2004). It has the advantages of
being computationally cheap and easily adjusted to new areas. A generic
weakness of statistical downscaling is the high demand on available data.
Therefore, it may appear to be an advantageous alternative for projects
where the computational capacity, technical expertise or time represent
significant restriction (Trzaska and Schnarr, 2014). In AEMET, two
statistical downscaling techniques- analog and regression methods- have been
applied to a large ensemble of climate projections released through the
World Climate Research Programme (WCRP) Coupled Model Intercomparison
project Phase 3 and Phase 5 (CMIP3 and CMIP5). Regression methods are
usually applied because they are easy to implement and computationally
efficient. Despite the potential problem that point out how statistical
relationships derived from observations or simulations of the past will
continue to be applicable under future climate conditions, it is considered
that statistically downscaled projections are sufficiently robust to make
available.

The downscaled projections are developed over (a) Iberian Peninsula and
Balearic Archipelago and (b) Canary Islands. Furthermore, regression was used
to downscale to the locations of selected weather stations. As regards the
analog method, it is based on synoptic analogue selection using sea level
pressure and 500 hPa wind components model simulation and application of
regression relationships to a selection of predictors (see Petisco de Lara
2008a, b; Amblar-Francés et al., 2017 for more details).

On the other hand, the AEMET portal provides entry information for AdapteCCa
(see Fig. 4), which is the Spanish Platform of Interchange and information
query about climate change adaptation in Spain
(http://escenarios.adaptecca.es, last access: 2 August 2018), with
zooming and filtering possibilities and a user friendly interface. AdapteCCa
is framed inside the LIFE-SHARA project (http://www.lifeshara.com/,
last access: 2 August 2018), which is boosting climate change adaptation in
Spain and Portugal. It was created as an initiative of the Spanish Climate
Change Office (OECC), the Biodiversity Foundation and their equivalents in
the Spanish autonomous communities.

Moreover, AdapteCCa national platform has been designed taking into full
consideration and seeking maximum synergy with the European Climate-Adapt
platform (https://climate-adapt.eea.europa.eu/, last access:
27 July 2018), which is an initiative of the European Commission to promote
access and exchange of information about adaptation on the different sectors
within the European policies and on the different Member States frameworks
and initiatives.

Figure 6Spatial distribution of climatological stations used in
the regionalization of temperature (374) and precipitation (2323) and
spatial distribution of gridpoints of Euro-CORDEX.

As part of AdapteCCa platform, a new visualization tool for climate change
scenarios over Spain has been developed. This Climate Scenarios viewer
allows to easily visualize and download data of the last generation of
regional climate change projections over Spain. There has been a relevant
increase in contents, in practice this means that visits and downloads of
the webpage have increased very significantly in last months. These evolving
needs, over time and focus, have been determined through consultation with a
wide range of users and has expanded to larger ensembles of indices and
extremes. It should be noted that the National Adaptation Plan to Climate
Change (PNACC) is the general framework for the activities of assessing
impacts, vulnerability and adaptation to climate change in Spain. In this
context, AdapteCCa contributes to reinforce the structure of the PNACC axis
of mobilization of actors and the pillar coordination between
administrations, being a complementary feature to the AEMET portal (Fig. 5).

AEMET, as a designated national expert agency for weather and climate in
Spain, has considerable experience of communicating, conveying key
information and concepts regarding climate change and climate scenarios to
an audience that is more and more interested and receptive. For the last few
years AEMET has worked closely with the Spanish Climate Change Office (OECC)
– responsible for the coordination, management and follow-up of the PNACC-
and PNACC partners, participating in several of the groups'
meetings, an area for intensive and fruitful exchange of knowledge and
perspectives.

A major challenge lies in the estimation and later visualization and
communication of uncertainties associated to the climate projections
generation. Before addressing this problem, we emphasize that a climate
projection is the simulated response of the climate system to a scenario of
future emission or concentration of GHGs and aerosols, generally derived
using climate models. Climate projections are distinguished from climate
predictions by their dependence on the emission/concentration/radiative
forcing scenario used, which is in turn based on assumptions concerning, for
example, future socio-economic and technological developments that may or may
not be realized
(https://www.ipcc.ch/pdf/assessment-report/ar5/syr/AR5_SYR_FINAL_Glossary.pdf,
last access: 25 July 2018).

Uncertainty is a feature that should not be ignored or side-lined, though it
is often the case that in science it is misinterpreted by the generic user as
ignorance. To address this issue, AEMET has decided to use multiple
realizations (ensembles) of regionalized projections, to allow an estimation
of uncertainties. In particular, focusing on the available data on the AEMET
portal, it is possible to estimate uncertainties associated to three sources:
different emission scenarios, different global models simulations, and
different regionalization techniques (see Amblar-Francés et al., 2017).
In other words, we have to face with the cascade of uncertainty in climate
projections, a visual scheme can be found in Wilby and Dessai (2010). They
illustrate the various steps in a “top-down” assessment of climate risks,
going from future society, through greenhouse gas emissions, GCM simulations,
regional scenarios, impact models and local impacts to an actual adaptation
response. The relative importance of the different uncertainties will depend
on timescale, region, impact, relevant climate variables and other potential
factors.

Vautard et al. (2013) argue that the medium term future period of 2050
corresponds to the societal demand of climatic projections useful for
adaptation purposes. Regardless of the time scope of the climate projections
of the range of possible future scenarios, important is the need for
downscaling scenarios and projections at spatial scales that are relevant
for adaptation policies. The visualization is potentially complementary to
other approaches to describe the relative importance of different sources of
uncertainty in climate projections (for instance, uncertainty tubes, as it
was referred in Chapter 11, AR5- IPCC representing the probability
distribution). The temporal scope of most of the impact studies based on
such climate change projections is the end of the century. Additionally, we
must account for uncertainties in the reaction of natural ecosystems and
human society to estimated climate change when we want to create regional
climate change projections and assessment of climate change impacts in
various sectors (Giorgi et al., 2009).

Still on the subject, a key factor in the strategy has been devoted to the
communication of uncertainties, taking into account that communicating
uncertainties to users is not an easy task. In the selection of figures,
mainly the evolution plots, we have insisted on the importance of the
uncertainties, for instance in Fig. 3, RCP scenario uncertainty is not
relevant for the first 50 year period for any RCP considered, but relevant
at the end of the century and also depending on the RCP chosen, so this is
why it is necessary to focus on the use of all available scenarios. Finally,
depending on the variable users are interested in, we guide them to choose
dynamical or statistical downscaling.

Focusing on the analysis of our strong points, we would stress the high
density of the Spanish climatological observations network (Fig. 6). In
contrast with other NMHSs, AEMET has made use for the reference climate
change downscaled scenarios of not only dynamical regionalization but also
statistical methods benefiting from the high density of the Spanish
climatological network. Additionally, the strong links maintained with the
main national users (water, energy, biodiversity, tourism) coordinated by
the OECC have shaped the way climate change projections are produced,
visualized and delivered.

Keeping in mind the importance of regionalized climate change projections
for impact studies on climate sensitivity sectors, AEMET in the frame of
PNACC-2017 will continue with the machinery of production, improving and
updating them applying and analyzing different bias correction methods.

From 2006 onwards, AEMET has routinely produced or adapted statistical and
dynamical downscaled climate projections for the PNACC community (based on
data from TAR, AR4, AR5, PRUDENCE, ENSEMBLES and EURO-CORDEX). There has
been a steady improvement including updates associated to IPCC cycles and
more products. In the future this service aims at complementing the
Copernicus Climate Change Service (C3S) in terms of resolution, expression
of uncertainty, visualization, tailored adjustments and reinforcement of
links with national users. The permanent contact with a wide range of
end-users, through e-mail, phone calls and specialized meetings, has allowed
us to be fully aware of the frequently need of help with data handling and
interpretation of products. The establishment of a long-term bidirectional
communication permitting feedback has revealed to be essential for meeting
the specific requirements from users. Our experience with users has shown us
some major practical difficulties associated with the use of climate
projections, such as time and space scale mismatch and the inconsistency
between data needs and their availability. The lack of tools supporting
decision making in a context of uncertainty prevents a wider use of
downscaled climate projections. Finally, we are fully aware of the need to
improve and progress on communication and dissemination actions, along with
the model improvements associated to new cycles of IPCC and more adequate
downscaling techniques.

The AEMET group working on regionalized climate change projections focuses its
activity on climate models evaluation, downscaling algorithms and
combination/synthesis of regionalized projections for impact and adaptation
studies over the Spanish domain. Specifically for this paper, MAPS and PRC
adapted and developed algorithms for two statistical methods (Analogs and
regression, respectively). MPAF prepared all input data, made most of the
calculation for downscaling of climate change projections including process
monitoring, quality control and postprocessing. Supervision and final
analysis of the projections obtained were conducted by MJCC and ERC, who also
verified trends and compared with other published studies. Finally, MAPS
selected the information presented in this paper, prepared the first draft
and made most of the editorial work.

This article is part of the special issue “17th EMS Annual
Meeting: European Conference for Applied Meteorology and Climatology 2017”.
It is a result of the EMS Annual Meeting: European Conference for Applied
Meteorology and Climatology 2017, Dublin, Ireland, 4–8 September 2017.

Special thanks are due to people involved in CMIP5, CMIP3, TAR, PRUDENCE,
ENSEMBLES, CORDEX and re-analyses projects. Thanks are due to the two
anonymous reviewers for their suggestions which improve substantially the
manuscript.

Christensen, J. H. and Christensen, O. B.: A summary of the PRUDENCE model
projections of changes in European climate during this century, Climatic
Change, 81, 7–30, https://doi.org/10.1007/s10584-006-9210-7, 2007.

The Spanish State Meteorological Agency (AEMET) has been producing since 2006 a set of reference downscaled regional climate change projections over Spain. Its strategy aims at exploiting all the available sources of information on climate change projections. In the future this service aims at complementing the Copernicus Climate Change Service (C3S) in terms of resolution, expression of uncertainty, visualization, tailored adjustments and reinforcement of links with national users.

The Spanish State Meteorological Agency (AEMET) has been producing since 2006 a set of reference...